MedDeID enables locally governed clinical-text de-identification from real or synthetic training data

TL;DR - MedDeID is an on-premises framework for training and deploying clinical-text de-identification models using real or synthetic notes. Its synthetic-only models achieved strong identifier recall, offering institutions a path to reuse sensitive clinical data without transferring it externally.

• A compact transformer trained on Dutch hospital data detected 98.9% of identifying text while redacting 0.24% of text outside annotated identifiers.
• The Dutch synthetic-only model detected 96.1% of identifiers and outperformed the hospital-trained model on primary-care notes in recall (90.3% versus 87.0%).
• Synthetic training improved robustness to changes in identifier formatting.
• An English synthetic-only implementation reached 99.7% and 98.9% character-level detection on two synthetic benchmarks, but clinical English performance was not established.